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test_cylinder_custom_sk.py
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test_cylinder_custom_sk.py
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# -*- coding:utf-8 -*-
# author: abhigoku10
# @file: train_cylinder_asym.py
import os
import time
import argparse
import sys
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
import pdb
from utils.metric_util import per_class_iu, fast_hist_crop
from dataloader.pc_dataset import get_SemKITTI_label_name,get_SemKITTI_label_color
from builder import data_builder, model_builder, loss_builder
from config.config import load_config_data
from utils.load_save_util import load_checkpoint
import warnings
warnings.filterwarnings("ignore")
def main(args):
pytorch_device = torch.device('cuda:0')
config_path = args.config_path
configs = load_config_data(config_path)
dataset_config = configs['dataset_params']
test_dataloader_config = configs['test_data_loader']
val_dataloader_config = configs['val_data_loader']
val_batch_size = val_dataloader_config['batch_size']
test_batch_size = test_dataloader_config['batch_size']
model_config = configs['model_params']
test_hypers = configs['test_params']
grid_size = model_config['output_shape']
num_class = model_config['num_class']
ignore_label = dataset_config['ignore_label']
model_load_path = test_hypers['model_load_path']
# model_save_path = test_hypers['model_save_path']
output_path=test_hypers['output_save_path']
SemKITTI_label_name = get_SemKITTI_label_name(dataset_config["label_mapping"])
unique_label = np.asarray(sorted(list(SemKITTI_label_name.keys())))[1:] - 1
unique_label_str = [SemKITTI_label_name[x] for x in unique_label + 1]
my_model = model_builder.build(model_config)
if os.path.exists(model_load_path):
my_model = load_checkpoint(model_load_path, my_model)
my_model.to(pytorch_device)
test_dataset_loader, val_dataset_loader = data_builder.build_valtest(dataset_config,
test_dataloader_config,
val_dataloader_config,
grid_size=grid_size)
hist_list = []
time_list = []
#####Testing inference
pbar = tqdm(total=len(test_dataset_loader))
print('#'*60)
print("Processing the Testing pipeline")
print("The length of the test dataset is {}".format(len(test_dataset_loader)))
print('#'*60)
print(len(test_dataset_loader))
with torch.no_grad():
print("Inside torch nograd function")
for i_iter_val, (_,test_vox_label,test_grid,test_pt_labs,test_pt_fea,test_index,filename) in enumerate(test_dataset_loader):
print("Inside for loop function")
print(" THe enumuerated values test_grid:{} test_pt_feat:{} test_index:{}".format(test_grid,test_pt_fea,test_index))
test_label_tensor = test_vox_label.type(torch.LongTensor).to(pytorch_device)
test_pt_fea_ten = [torch.from_numpy(i).type(torch.FloatTensor).to(pytorch_device) for i in
test_pt_fea]
test_grid_ten = [torch.from_numpy(i).to(pytorch_device) for i in test_grid]
print("Passing the data into the trained model")
predict_labels = my_model(test_pt_fea_ten, test_grid_ten,test_batch_size)
predict_labels = torch.argmax(predict_labels, dim=1)
predict_labels = predict_labels.cpu().detach().numpy()
# write to label file
for count,i_test_grid in enumerate(test_grid):
test_pred_label = predict_labels[count,test_grid[count][:,0],test_grid[count][:,1],test_grid[count][:,2]]
test_pred_label = np.expand_dims(test_pred_label,axis=1)
# print(" The test labels befor conversion {}".format(max(test_pred_label, dim=1)))
# save_dir = test_dataset_loader.im_idx[test_index[count]]
_,dir2 = filename[0].split('/sequences/',1)
new_save_dir = output_path + '/sequences/' +dir2.replace('velodyne','predictions')[:-3]+'label'
if not os.path.exists(os.path.dirname(new_save_dir)):
try:
os.makedirs(os.path.dirname(new_save_dir))
except OSError as exc:
if exc.errno != errno.EEXIST:
raise
test_pred_label=get_SemKITTI_label_color(dataset_config["label_mapping"],test_pred_label)
test_pred_label = test_pred_label.astype(np.uint32)
# print(" The test labels after conversion {}".format(max(test_pred_label, dim=1)))
test_pred_label.tofile(new_save_dir)
# ##### To check the predicted results
# for count, i_test_grid in enumerate(test_grid):
# hist_list.append(fast_hist_crop(predict_labels[
# count, test_grid[count][:, 0], test_grid[count][:, 1],
# test_grid[count][:, 2]], test_pt_labs[count],
# unique_label))
pbar.update(1)
# iou = per_class_iu(sum(hist_list))
# print('*'*80)
# print('Testing per class iou: ')
# print('*'*80)
# for class_name, class_iou in zip(unique_label_str, iou):
# print('%s : %.2f%%' % (class_name, class_iou * 100))
# test_miou = np.nanmean(iou) * 100
# print('Current test miou is %.3f ' % test_miou)
print('Exiting the for loop function')
print('Inference time per %d is %.4f seconds\n' % (test_batch_size,np.mean(time_list)))
del test_vox_label, test_grid, test_pt_fea, test_grid_ten,test_index
print('Exiting the test functino')
pbar.close()
if __name__ == '__main__':
# Testing settings
parser = argparse.ArgumentParser(description='')
parser.add_argument('-y', '--config_path', default='config/semantickitti.yaml')
args = parser.parse_args()
print(' '.join(sys.argv))
print(args)
main(args)